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Abstract

Background

This study evaluated the measurement properties of a newly developed instrument –
the Self-Management Profile for Type 2 Diabetes (SMP-T2D).

Conclusions

Two studies provide preliminary evidence regarding the reliability, validity and responsiveness
of the SMP-T2D. Further research on the utility of the instrument is needed.

Background

Type 2 diabetes mellitus can have substantial effects on patients’ physical and psychosocial
well-being. People with diabetes, their families and health care providers, and health
policymakers are interested in improving diabetes outcomes, which requires both medical
care and patient self-management. As such, the American Diabetes Association (ADA)
Standards of Medical Care has identified self-management education and ongoing support
as integral components of diabetes care, and recommends monitoring self-management
behaviors in patients receiving treatment for diabetes [1].

Given the importance of patient self-management in the control of diabetes, evaluations
of effectiveness in clinical trials of behavioral and pharmacologic treatments should
include an assessment of diabetes self-management, augmenting physiological outcomes
such as blood glucose and patient reported outcomes (PROs) such as health-related
quality of life. Better self-management is expected to lead to improved glycemic control
and better patient outcomes in weight management and diabetes-related distress. Therefore,
we initiated a project to develop and validate a new patient self-report instrument,
the Self-Management Profile for Type 2 Diabetes (SMP-T2D), which would assess multiple
domains and dimensions of self-management and would be brief enough for use in clinical
trials.

The US Food and Drug Administration (FDA) has provided guidance on the development
of PRO instruments for use in evaluating medical products for labeling claims [2]. Data used for labeling claims must meet the most stringent standards and these standards
define best practice. The FDA guidance requires documentation of patient input into
instrument development, and the development of the SMP-T2D used patient input to assess
the relevance of the concepts reflected in the instrument as well as patient comprehension
and interpretation of individual items.

This paper reports the development of the SMP-T2D and the results of two studies evaluating
the measurement properties of the SMP-T2D in the clinical trial setting.

Methods

Concept development

Concepts to be included in the SMP-T2D development process were based on a systematic
review of the diabetes self-management literature. The American Diabetes Association
and the American Association of Diabetes Educators (AADE) have developed standards
for the provision and evaluation of diabetes self-management education and support
[1,3]. These standards identified seven domains (the AADE7) that are the essential components
of self-management: physical activity, healthy eating, medication taking, blood glucose
monitoring, problem solving, risk reduction, and coping (psychosocial adaptation)
[4,5]. A self-administered questionnaire, the Diabetes Self-Management Assessment and Report
Tool (D-SMART), was developed to assess the AADE7 [6-8].

The D-SMART was developed to provide a detailed assessment that could be used to guide
comprehensive self-management education. But this instrument is too lengthy to be
used in clinical trials of diabetes treatment medications. Of the seven AADE domains,
two have less relevance to the clinical trial situation. Risk reduction involves efforts
to prevent long-term complications, e.g., by having periodic tests and examinations,
and actions such as vaccinations, prophylactic aspirin, foot checking, etc. Although
important, these are not targets of diabetes medication trials. Problem solving relates
to dealing with situations that are likely to be covered by clinical trial protocols
and/or to be managed by clinicians on the research team. Therefore, these two domains
were not included in the instrument development process.

The D-SMART also incorporates dimensions believed to be important in patient implementation
of self-management, including confidence in managing diabetes, barriers to self-care,
and self-care goals. While patient goals are important in routine clinical care, in
clinical trials the goals are generally defined by the trial, and so the SMP-T2D was
not designed to assess personal goals. The concept of barriers has been identified
as an important factor in self-management [6-10]. Instead of an inventory of barriers that includes multiple questions (as in the
D-SMART), we focused on the consequences of barriers for patients, i.e., the degree to which barriers make it difficult to implement self-management.
Self-management difficulty has been used as a way of measuring self-management performance
[11]; this strategy recognizes that patients may implement self-management even though
it is difficult, but the behavior is less likely to be sustained when it is difficult.
Moreover, patients may be more willing to admit to having difficulty with self-management
than to admit not implementing their prescribed regimen [12].

Self-efficacy has been viewed as a key construct in human behavior and was identified
as another essential component in our measure of self-management [13,14]. Finally, in an effort to develop personalized weighting for the SMP-T2D measures,
we included importance ratings for the five behavioral domains [15].

Following the identification of concepts from the existing literature, we developed
a focus group interview guide to assess the relevance of the identified concepts and
to identify new concepts. Four focus groups were conducted with a total of twenty-four
participants with type 2 diabetes. Participants had a diagnosis of diabetes ranging
from 1.5 to 30 years, with an average of 11.2 years. Most participants (66.7%) had
an A1c between 7 and 11. Participants were taking either injectable (n = 12) or oral
(n = 12) diabetes medications. The specific medication regimens varied greatly among
participants.

One of the authors (MM) developed an initial coding framework from the focus group
transcripts that linked to the literature and existing knowledge about self management.
Next, two interviewers were trained to code concepts using this initial coding framework.
After the first two transcripts were coded, ATLAS.ti software [16] was used to tag the assigned concept codes in each transcript. Results were reviewed
by the coding team, and revisions to the coding framework were made keeping the focus
group data in mind. Saturation was reached after the third focus group, as no new
concepts emerged.

The behavior measure for the concept of coping uses the same measurement logic as
the D-SMART measure of coping [6], assessing the consequences of coping (i.e., amount of diabetes-related frustration and worry about future health)
rather than coping behavior per se. Assessing coping behavior would require an inventory of effective coping strategies that would be beyond the
scope of a measure to be used in clinical trials. The Ease of Coping item specifically
refers to “coping with frustration and worry related to your diabetes.”

The “Ease of Managing Weight” was an outcome or benefit not contained in the AADE7.
The content supporting this item emerged during the qualitative phase of the project
as highly relevant to patients. Since this concept is also identified in the ADA guidelines
as an important aspect of diabetes self-management, it was included in the instrument.

During the development phase, an importance item (“How important is it for you right
now to…”) was included for each of the five behaviors and ease of managing weight
in order to assess the potential of deriving weights for each domain score.

Cognitive interviews [18] were conducted with 34 patients with type 2 diabetes to assess the comprehension
and interpretation of the draft instrument. The draft used in the validation studies
incorporated revisions based on that process (see Appendix). Participants in the cognitive
interviews had a diagnosis of diabetes ranging from 1 to 30 years, with an average
of 10.7 years. Most participants (76.5%) had an A1c between 7 and 11. The specific
medication regimens varied greatly among participants; 17 participants were using
oral medications only, 16 participants were using injectable medications alone or
in combination with oral medications, and one participant was managing diabetes with
diet and exercise.

Scoring

All SMP-T2D scores were transformed to a 0–100 scale, with equal increments between
responses. Scoring was designed so that higher scores indicate better self-management
(as a result, items rating “difficulty of…” are reverse scored and labeled as “ease
of…”). Except for the Physical Activity behavior measure scores for domains that had
more than a single item were calculated as the mean of the available items. The measure
of Physical Activity behavior consists of 3 items, one each addressing light, moderate,
and vigorous activity. Scoring followed the logic of the Rapid Assessment of Physical
Activity (RAPA) [19], where respondents’ scores are categorized into one of four levels of physical activity:
sedentary (0 = no days of vigorous or moderate activity, and less than 2 days of light
activity); underactive, light activity (33.3 = no days of vigorous activity with 1–2 days
of moderate activity and/or 2 or more days of light activity); underactive, regular
activity (66.7 = 1–2 days of vigorous activity and/or 3–4 days of moderate activity);
and active (100 = 3 or more days of vigorous activity and/or 5 or more days of moderate
activity).

Participants and procedures for validation studies

There were two validation studies; participant characteristics are reported in Table 1. In Study 1 the SMP-T2D was administered at the end of an ongoing clinical trial
to evaluate its measurement properties. The parent trial was a randomized three-arm
study designed to compare the effects of exenatide once weekly (QW) to sitagliptin
and pioglitazone over 26-weeks [20,21]. Glucose monitoring was not specified by protocol.

Participants were adults diagnosed with type 2 diabetes and treated with metformin.
A1c values at screening could range between 6.5% and 10.5% with body mass index (BMI)
ranging between 25 and 45 kg/m2. Participants previously treated with other diabetes medications were excluded.

The Study 1 protocol was amended to include administration of the SMP-T2D after some
participants had already completed the 26-week controlled study; 147 of the 243 English-speaking
participants who had not completed the trial at 26 weeks were administered the SMP-T2D
at week 26 (for cross-sectional validation) and week 27 (for reproducibility assessment).
These 147 participants comprise the study population for this study.

For Study 2 the parent trial was a randomized four-arm study designed to compare the
effects of different doses of exenatide once weekly or once monthly [22]. Glucose monitoring was not specified by protocol. Participants were 93 adults diagnosed
with type 2 diabetes, who either were not taking diabetes medication or were treated
with metformin and/or thiazolidinedione. A1c values at screening could range between
7.1% and 11.0%, and body weight was stable for at least 3 months prior to screening.

Validation measures

All measures of constructs used to evaluate the construct validity of the SMP-T2D
were those included in the parent study protocols and were obtained at the same times
the SMP-T2D was administered (except for the SMP-T2D retest in Study 1). These trials
did not have parallel measures of the constructs assessed by the SMP-T2D (e.g., adherence,
self-efficacy, barriers, etc.).

Clinical assessments in both studies included clinician-reported BMI and the biomarker
A1c. In Study 1 five PRO construct validity instruments were administered. EuroQoL-Five Dimensions (EQ-5D)[23]: the validation analyses used only the item measuring severity of problems performing
usual activities (which was treated as a set of ordered categories for analysis purposes)
and the visual analog scale (VAS) measuring patients’ perceived health status, the
former because it provides validation for physical activity domain and the latter
because the VAS has greater sensitivity than the health utility index [24]. Impact of Weight on Quality of Life Questionnaire, Lite Version (IWQOL-Lite)[25]: the validation analyses used only the total score, ranging from 0 (low) to 100 (high).
World Health Organization-Five Well-being Index (WHO-5)[26]: scores were standardized to a 0–100 range, with higher scores representing positive
psychological well-being. Diabetes Treatment Satisfaction Questionnaire (Status Version, DTSQs)[27]: the validation analyses used only the items measuring perceived frequency of hypoglycemia
and perceived frequency of hyperglycemia. These DTSQs items were also used in Study
2.

All studies were approved by an Institutional Review Board and were conducted in accordance
with ethical principles described in the Declaration of Helsinki.

Statistical analyses

All analyses were performed using the Statistical Package for the Social Sciences
(SPSS), Release 11.5.0 [28].

Descriptive statistics

In addition to the mean and standard deviation for each SMP-T2D measure, the percentage
of persons giving the minimum and maximum scores were used to determine floor and
ceiling effects.

Internal consistency reliability

For the measures that contained multiple items, Cronbach’s alpha [29] was used to assess the degree to which the set of items measured a single unidimensional
latent construct. An alpha equal to or greater than 0.70 is defined as adequate internal
consistency [30].

Test-retest reliability

The extent to which the SMP-T2D measures have stable scores over time in the absence
of true change was assessed with two administrations of the SMP-T2D at a one-week
interval where change was not expected. One test used the average Spearman-Brown coefficient;
a coefficient equal to or greater than 0.70 is defined as adequate test-retest reliability
[30]. The t-test was used for the change in mean scores over time (represented in baseline
standard deviation units to permit an assessment of effect size [31]); smaller change indicates greater reliability, and a difference of >0.5 standard
deviation units indicates a minimum detectable difference [32].

Construct validity

Construct validity was assessed through correlations between measures representing
constructs that were hypothesized to be more strongly (convergent) or more weakly
(discriminant) related according to a priori expectations based on the theoretical relationships among constructs [33].

The first phase of validity analysis examined a correlation matrix of associations
among the SMP-T2D measures; this analysis was performed only for the Study 1 data
because the Study 2 data was regarded as redundant. Measures for each of the five behaviors were predicted to be more strongly associated
with their corresponding measures of perceived ease than with other items that did
not correspond with each other. Two more specific convergent/discriminant validity hypotheses were formulated: (1) Ease of Managing Weight will be more strongly associated with Ease of Eating Healthy
and Ease of Physical Activity than with Ease of Medication Taking Adherence and Ease
of Glucose Monitoring, and the association with Ease of Coping would be intermediate;
(2) Confidence with Ability to Manage Diabetes will be more strongly associated with
Coping and Ease of Coping than with measures of behavior or perceived ease from other
domains.

The second phase of validity analysis examined the associations between the SMP-T2D
measures and the construct validity measures. All SMP-T2D measures were predicted to be positively associated with better scores
on all construct validity measures. However, construct validity measures were predicted to be more strongly associated with lifestyle
domains (Eating Healthy, Physical Activity and Coping) than with medical domains (Medication
Taking and Glucose Monitoring); rationale – in a clinical trial setting, medication taking is constrained and the
other factors are likely to have greater impact on outcomes, or to be affected by
those outcomes. Moreover, construct validity measures were predicted to be more strongly associated with measures
of perceived ease of behavior than measures of behavior frequency; rationale – ease of a behavior is a more sensitive indicator of the degree to which
a behavior is performed and may be less subject to reporting bias due to social desirability
(i.e., it is easier to admit to difficulty than non-adherence). Finally, construct validity measures were predicted to be more strongly associated with Coping
measures, Confidence in Ability to Manage Diabetes and Ease of Managing Weight than
with frequency and perceived ease of Medication Taking, Glucose Monitoring, Eating
Healthy and Physical Activity; rationale – the former measures represent more global constructs that incorporate
multiple specific behavioral domains.

Ability to detect change

Ability of the instrument to detect change over time was assessed in Study 2. All
Study 2 participants were changing their medication and were included in the analyses.
The first type of analysis assessed change in mean SMP-T2D scores over time. It was hypothesized that there would be improvement in the Eating Healthy and Coping
measures, Ease of Managing Weight and Confidence in Ability to Manage Diabetes but
not other SMP-T2D measures; rationale – the effects of exenatide are strongest for eating, weight and glycemic
control [34] (the intervention did not target glucose monitoring or physical activity, and patients
switched from oral to injectable medication).

The second type of validity analysis assessed the association between change in SMP-T2D
scores and change in validation measures over the course of the study. Validity measures were predicted to be most strongly associated with the Coping measures,
Confidence in Ability to Manage Diabetes and Ease of Managing Weight than with other
measures; rationale – the former measures represent more global constructs that incorporate
multiple specific behavioral domains. BMI was predicted to be more strongly associated with dietary and physical activity
measures than with measures of glucose monitoring and medication taking; rationale – weight is more dependent on caloric intake and expenditure than other
self-management behaviors.

A supplementary assessment of ability to detect change used a stepwise regression
analysis of the change scores for each of the 4 validity measures to determine the
independent relationships with SMP-T2D change scores, controlling for the baseline
value of the validity measure.

Results

The time needed to complete the SMP-T2D across patients ranged from 3 to 5 minutes.
Readability analyses of the pre-final SMP-T2D were conducted in Microsoft Word® 2003
and showed a Flesch-Kincaid Grade Level score of 7.0.

Preliminary analysis of the six importance ratings in Study 1 showed all domains being
equally “Very Important” and therefore these items were not examined further. These
items were not included in Study 2.

Scaling

Means for SMP-T2D measures ranged between 62 and 97 across the two studies; only the
Medication Taking frequency and ease measures had means above 85 (see Table 2). Floor effects were low, with a high of 11.8% having the minimum score for Glucose
Monitoring behavior in Study 2 (interpolated median for both studies = 2.5%). Ceiling
effects were more pronounced, with a high of 89.2% having the maximum score for Ease
of Medication Taking in Study 2 (interpolated median for non-medication domains in
both studies = 33.0%).

Intercorrelations of SMP-T2D measures

Table 3 presents the associations within and between the domains of the SMP-T2D in Study
1. All convergent/discriminant validity hypotheses internal to the instrument were
confirmed. As expected, the corresponding ease and behavior measures had stronger
associations than those that do not correspond; all corresponding measures but only
2 of 20 non-corresponding measures correlated at p <; 0.001. Secondly, Ease of Managing
Weight was more strongly associated with Ease of Eating Healthy and Ease of Physical
Activity than with measures of perceived ease from other domains. Finally, Confidence
with Ability to Manage Diabetes was more strongly associated with consequences of
Coping and Ease of Coping than with measures of behavior or perceived ease from other
domains.

Convergent and discriminant validity

Table 4 presents the associations between baseline scores on the SMP-T2D measures and the
construct validity measures in Study 1. All convergent/discriminant validity hypotheses
were generally confirmed, although there were a few exceptions to each of the predicted
patterns. Nine of twelve SMP-T2D measures were significantly associated with one or
more validity measures and all significant associations were in the predicted direction.

As predicted, all construct validity measures were more strongly associated with measures
from the lifestyle domains (frequency and perceived ease of Eating Healthy, Physical
Activity and Coping) than with measures from the medical regimen domains (frequency
and perceived ease of Medication Taking and Glucose Monitoring). And most construct
validity measures were more strongly associated with frequency and Ease of Coping
than with frequency and perceived ease of Eating Healthy and Physical Activity. Additionally,
with few exceptions all validity measures were more strongly associated with perceived
ease of behavior than measures of behavior frequency. Finally, most construct validity
measures were more strongly associated with Confidence in Ability to Manage Diabetes
and Ease of Managing Weight than with frequency and perceived ease of Medication Taking,
Glucose Monitoring, Eating Healthy and Physical Activity.

Eight SMP-T2D measures were significantly associated with one or more validity measures
and all significant associations were in the predicted direction (see Table 5). For the four validity measures in common between Study 1 and 2, there was an 81%
between-study concordance for statistical significance/non-significance of correlations
with the SMP-T2D measures.

Ability to detect change

In Study 2 six of twelve SMP-T2D measures showed during-study improvement (see Table 2). As hypothesized, SMP-T2D measures relating to eating and weight, coping and confidence
in managing diabetes exhibited statistically significant improvements (the only exception
was Ease of Coping). Two measures (consequences of Coping behavior and Confidence
in Managing Diabetes) had effect sizes near or above the 0.50 cut-off for a minimal
detectable difference [32].

In Study 2, improvements in six SMP-T2D measures were associated with improvement
in a trial outcome; none were associated with deterioration in an outcome (see Table 5).

Stepwise regression analysis of the change scores for each of the 4 validity measures
assessed the independent relationships with change scores for the SMP-T2D measures,
controlling for the baseline value of the validity measure. Change in Confidence in
Managing Diabetes (beta = −0.27, p <; 0.01) and Eating Healthy behavior (beta = −0.20,
p <; 0.05) accounted for 11.5% of the variance in change in A1c. Change in Physical
Activity behavior and Glucose Monitoring behavior accounted for 11.2% of the variance
in change of perceived frequency of hypoglycemia; these factors had offsetting effects,
with increased physical activity associated with increased hypoglycemia (beta = 0.23,
p <; 0.05) and increased Glucose Monitoring associated with reduced hypoglycemia (beta
= −0.29, p <; 0.01). Change in Confidence in Managing Diabetes (beta = −0.16, p <;
0.05) accounted for 5.2% of the variance in change of perceived frequency of hyperglycemia.
Change in Ease of Physical Activity (beta = −0.23, p <; 0.05) accounted for 2.5% of
the variance in change in BMI.

Discussion

Development of the SMP-T2D incorporated self-management concepts that patients with
type 2 diabetes expressed as important in their experience with diabetes and its treatment.
The specific items included in the SMP-T2D were generated using language as close
as possible to that used by patients. Patients indicated a high level of understanding
of the questionnaire items in cognitive interviews before the psychometric properties
were assessed. This developmental process provides support for the content validity
of the SMP-T2D and justification for the conceptual framework.

The results of the validation studies provide preliminary support for the reliability
and validity of the SMP-T2D. Internal consistency for multiple-item measures was acceptable
and test-retest reliability was adequate for all but two measures. The unexpected
low test-retest reliability for the frequency of medication taking measure (days missed
of prescribed diabetes medications during the past week) could be explained by the
lack variability due to the clinical trial treatment context. The majority of participants
adhered to their medication regimen, allowing outliers to skew the reliability estimate.
This item should be retained for further research within later phase studies in which
patient medication taking behavior is not as tightly controlled.

The modest test-retest reliability of the measure of physical activity behavior is
surprising as the measure of time-specific reliability (inter-item agreement) was
good. This may be due to the fact that the composite measure uses thresholds rather
than continuous increments of the component items so that small changes in activity
could yield substantial changes in scores. It is likely that physical activity varies
for most people in the short term (e.g., on a daily basis), and the measurement of
physical activity using threshold activity levels requires a longer reference time
period in order to stabilize short-term fluctuations in behavior.

Construct validity hypotheses were generally supported, with correlation patterns
as expected, but with correlations no more than moderate in strength. The most interesting
findings emerged from the correlations between SMP-T2D measures and the construct
validation measures. Validity measures were more strongly correlated with ease of
performing self-management behaviors than with frequency of performing the same behaviors.
This suggests that ease/difficulty is a more sensitive measure of self-management
or that patients are more willing to admit to having difficulty with self-management
than to admit not implementing their prescribed regimen. The one exception was for
coping, but this may be because coping behavior was measured as the consequences of
coping rather than the frequency of coping behavior.

The strongest findings regarding validity were obtained from the longitudinal analyses.
Change in SMP-T2D measures was moderately associated with change in the validity measures,
as predicted. In particular, multiple SMP-T2D measures predicted change in A1c and
perceived frequency of hypoglycemia. Perhaps the most interesting finding was that
two SMP-T2D measures were related to change in hypoglycemia in opposite directions;
increased physical activity increased hypoglycemia and increased glucose monitoring
lowered hypoglycemia.

The importance rating items did not yield any clear evidence of any one domain being
more important than another; rather, all domains were deemed very to extremely important.
Results may also reflect the selection of respondents into a clinical trial, as participants
are likely to be those with a strong belief in the importance of treatment. Importance
might be considered for further testing and inclusion in observational or non-clinical
trials, as variations in patient beliefs about the importance of performing various
regimen behaviors may be useful in informing clinical practice. Also, the measurement
approach might be modified so that respondents rank order the importance of behaviors rather than rate them in order to increase discrimination.

Study strengths and limitations

One of the strengths of the research reported here is that it involved multiple independent
studies. Following FDA guidance, there was an initial study to obtain patient input
about the measures to be included to represent the key domains, and cognitive debriefing
provided patient input about item content/wording. Then an initial validation study
generated information about psychometrics and was used to select final items for inclusion
in the version used to assess sensitivity to change and predictive validity.

Limitations of the research reported here include the fact that participants in clinical
trials tend to differ from the larger patient population in a number of ways. Moreover,
neither of the validation studies was designed specifically for validation of the
SMP-T2D (e.g., parallel measures of the SMP-T2D constructs were not used, reducing
the size of validation correlations). A trial that attempted to change behavior in
specific self-management domains (e.g., comparing usual care with an intervention
designed to improve healthy eating or physical activity) might have provided a more
rigorous test of the ability of the SMP-T2D to detect changes in behavior. Alternatively,
a longitudinal observational study might have provided a better opportunity for testing
the association between changes in SMP-T2D assessments of medication taking behavior
and clinical outcomes.

In future research, it may be prudent to expand the topics covered by the SMP-T2D.
Specifically, the current version assesses only one benefit of treatment – weight
management. Research has shown that other aspects of treatment are important to patients,
including burden of treatment (convenience, comfort, embarrassment), clinical efficacy
(ability to control hyperglycemia), and safety (avoidance of hypoglycemia) [34,35]. Incorporating these dimensions into the SMP-T2D would broaden its applicability
and could enhance its potential value as a research and clinical tool.

Conclusions and implications

The psychometric performance of the SMP-T2D in these trial-based analyses suggests
that it could be an important addition to the compendium of instruments used to assess
diabetes self-management and its contribution to treatment outcomes. It was quickly
completed by patients reading at a seventh grade level and its inclusion in the clinical
trials had minimal impact on trial management.

The SMP-T2D was developed for use in clinical trials; although generalizability outside
clinical trials has yet to be determined a potential strength of the instrument is
that the SMP-T2D concepts are also relevant in observational research. The SMP-T2D
also might have potential as a discussion tool to improve communication between patients
and clinicians in choosing and implementing treatment regimens.

Appendix 1

Content of the Self-Management Profile for Type 2 Diabetes (SMP-T2D)

1. How many days during the past week (last 7 days) did you miss taking your diabetes
medications as prescribed? {0–7, reverse scored}

2. How many days during the past week (last 7 days) did you miss monitoring your blood
sugar? {0–7, reverse scored}

3. How many days during the past week (last 7 days) did you eat foods not healthy
for your diabetes? {0–7}

4. During the past week (last 7 days), how many days did you eat more food than you
were supposed to? {0–7, reverse scored}

5. How many days during the past week (last 7 days), did you do at least some light
physical activity (such as walking, light gardening)? {0–7}

6. How many days during the past week (last 7 days), did you do at least 30 minutes
of moderate physical activity (such as pushing a vacuum cleaner, riding a bicycle,
playing golf)? {0–7}

7. How many days during the past week (last 7 days), did you do at least 20 minutes
of vigorous physical activity (such as running or participating in strenuous sports)?
{0–7}

8. During the past week, how much difficulty did you have with: {A great deal, A lot,
Moderate, A little, No}

9. During the past week (last 7 days), how frustrated have you been with trying to
manage your diabetes? {Not at all, Slightly, Moderately, Very, Extremely}

10. During the past week (last 7 days), how worried have you been about your future
health because of your diabetes? {Not at all, Slightly, Moderately, Very, Extremely}

11. Overall, how confident have you felt during the past week (last 7 days) about
being able to manage your diabetes? {Not at all, Slightly, Moderately, Very, Extremely}

12. How important is it for you right now to: {Lot, Moderate, Little, No}

a. monitor your blood sugar?

b. take your diabetes medications as your doctor instructed?

c. manage your weight?

d. manage your diet?

e. manage your physical activity?

f. manage frustration and worry related to your diabetes?

NOTE: Question 12 was not included in the version of the SMP-T2D used in Study 2.

Competing interests

This work was supported by Amylin Pharmaceuticals, Inc., San Diego, CA. DMB, MLM,
MP and DLP have received funding from Amylin Pharmaceuticals, Inc. JHB and AC are
employees of Amylin Pharmaceuticals, Inc. DLP has received honoraria from Amylin Pharmaceuticals,
Inc. for participating in the development of the instrument, analysis of the data
and preparation of this manuscript. MP has received honoraria from Amylin Pharmaceuticals,
Inc. for participating in the analysis of the data and preparation of this manuscript.

Authors’ contributions

MP provided input on the SMP-T2D development, participated in planning the analyses,
performed statistical analyses and helped draft the manuscript. DMB performed statistical
analyses and helped draft the manuscript. JHB participated in planning the study designs,
participated in the SMP-T2D development, and helped draft the manuscript. MLM participated
in the SMP-T2D development and helped draft the manuscript. AC helped draft the manuscript.
DLP participated in the SMP-T2D development, participated in planning the analyses
and helped draft the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors thank Richard Rubin and William Polonsky for comments on previous versions
of the SMP-T2D and this manuscript. Funding for this research and manuscript was provided
by Amylin Pharmaceuticals, Inc.

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